Climate change disproportionately affects people in low-and middle-income contexts who have done the least to contribute to it. Yet often these communities do not have the information they need to make informed decisions about how to mitigate or prepare for climate change. Machine learning holds great promise to advance efforts across a variety of sectors to understand, mitigate, and adapt to climate change.
However, particularly in low- and middle-income contexts globally, the effective use of machine learning is hampered by a lack of ground truth data accessible to all. For example, global models of the impact of rising precipitation on malaria incidence are blatantly incorrect in some parts of the world because they are missing local data. A lack of data about how extreme heat events are affecting human health is preventing policymakers from preparing their communities to respond to these events. AI and remote sensing could help map energy infrastructure needs and enable us to effectively deploy renewable energy globally, but many parts of the world that would benefit the most from such technologies do not currently have the data to power them.
Lacuna Funding
Lacuna Fund supports dataset creation, aggregation, and maintenance for the training and evaluation of machine learning models by and for local communities most affected by climate change in two tracks:
- Understanding climate harms to health and livelihoods
- Improving energy systems and infrastructure for climate change mitigation and adaptation
With a geographic focus on low- and middle-income countries in Africa, Asia, and Latin America, the call will put resources directly into the hands of actors in affected areas and ensure solutions are developed locally and centered on community needs and priorities. Both the Climate & Health and Climate & Energy tracks will open calls for proposal processes in April of 2022.
Open RFPs
See information on how to apply as well as open and past RFPs here.
Funders
The 2022 call for proposals on climate is supported by The Rockefeller Foundation, the German Federal Ministry for Economic Cooperation and Development (BMZ), Wellcome, and Google.org.